Modeling trends in crop yields

ABSTRACT

A method and system for modeling trends in crop yields is provided. In an embodiment, the method comprises receiving, over a computer network, electronic digital data comprising yield data representing crop yields harvested from a plurality of agricultural fields and at a plurality of time points; in response to receiving input specifying a request to generate one or more particular yield data: determining one or more factors that impact yields of crops that were harvested from the plurality of agricultural fields; decomposing the yield data into decomposed yield data that identifies one or more data dependencies according to the one or more factors; generating, based on the decomposed yield data, the one or more particular yield data; generating forecasted yield data or reconstructing the yield data by incorporating the one or more particular yield data into the yield data.

BENEFIT CLAIM

This application claims the benefit under 35 U.S.C. § 120 as aContinuation of application Ser. No. 15/017,370, filed Feb. 5, 2016, theentire contents of which is hereby incorporated by reference for allpurposes as if fully set forth herein. The applicants hereby rescind anydisclaimer of claim scope in the parent applications or the prosecutionhistory thereof and advise the USPTO that the claims in this applicationmay be broader than any claim in the parent applications.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent file or records, but otherwise reserves all copyright orrights whatsoever. © 2015 The Climate Corporation.

FIELD OF THE DISCLOSURE

The present disclosure generally relates to computer systems useful inagriculture. The disclosure relates more specifically to computersystems that are programmed or configured to model trends in yield ofcrops harvested from agricultural fields.

BACKGROUND

The approaches described in this section are approaches that could bepursued, but not necessarily approaches that have been previouslyconceived or pursued. Therefore, unless otherwise indicated, it shouldnot be assumed that any of the approaches described in this sectionqualify as prior art merely by virtue of their inclusion in thissection.

Data measurements representing yields of crops harvested fromagricultural fields are usually collected via a measurement process. Themeasurement process typically is a stochastic process that is prone toerrors and generalizations. For example, the received data measurementsmay be incomplete or inaccurate. Errors may be introduced at all levelsand the errors may often be unavoidable. For example, even if reports onyields of crops are generated based on reports obtained using asophisticated survey process at the county level, the reports may stillbe missing critical data.

The survey process also may be inaccurate and may fail to take intoconsideration the fact that measurements of yields of crops harvestedfrom one agricultural field may depend on crops harvested from theneighboring fields. Furthermore, the measurements may not reflect thefact that the crops harvested from one field may be influenced by thelocal microclimate and irrigation practices specific to that field, butnot to other fields.

SUMMARY OF THE DISCLOSURE

The appended claims may serve as a summary of the disclosure.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 illustrates an example computer system that is configured toperform the functions described herein, shown in a field environmentwith other apparatus with which the system may interoperate.

FIG. 2 illustrates two views of an example logical organization of setsof instructions in main memory when an example mobile application isloaded for execution.

FIG. 3 illustrates a programmed process by which the agriculturalintelligence computer system generates one or more preconfiguredagronomic models using agronomic data provided by one or more externaldata sources.

FIG. 4 is a block diagram that illustrates a computer system upon whichan embodiment of the invention may be implemented.

FIG. 5 is a flow diagram that depicts an example method for modelingtrends in crop yields.

FIG. 6 depicts a block diagram that depicts an example of a singularvalue decomposition approach for decomposing a large set of crop yieldvalues into smaller subsets that approximate the large set of crop yieldvalues.

FIG. 7 is a flow diagram that depicts an example method for modelingtrends in crop yields.

FIG. 8 is a data plot that depicts an example modeling function of cropyields.

FIG. 9 is a plate diagram of an example conditional autoregressivemodel.

DETAILED DESCRIPTION

Embodiments are disclosed in sections according to the followingoutline:

-   -   1. GENERAL OVERVIEW    -   2. EXAMPLE AGRICULTURAL INTELLIGENCE COMPUTER SYSTEM        -   2.1. STRUCTURAL OVERVIEW        -   2.2. APPLICATION PROGRAM OVERVIEW        -   2.3. DATA INGEST TO THE COMPUTER SYSTEM        -   2.4. PROCESS OVERVIEW—AGRONOMIC MODEL TRAINING        -   2.5 IMPLEMENTATION EXAMPLE—HARDWARE OVERVIEW    -   3. CHARACTERISTICS OF MEASUREMENTS OF YIELDS OF CROPS    -   4. MODELING TRENDS IN YIELDS OF CROPS        -   4.1. MODELS FOR REPRESENTING YIELDS OF CROPS        -   4.2 LINEAR MODEL    -   5. SINGULAR VALUE DECOMPOSITION    -   6. FORECASTING AND RECONSTRUCTING MEASUREMENTS OF YIELDS OF        CROPS    -   7. MODIFIED CONDITIONAL AUTOREGRESSIVE APPROACH    -   8. EXAMPLE OF COMBINED APPROACHES    -   9. EXAMPLE RESULT ANALYSIS    -   10. ADDITIONAL APPROACHES        -   10.1. MEAN MODEL APPROACH        -   10.2. LINEAR MODEL APPROACH        -   10.3. ROBUST LINEAR MODEL APPROACH        -   10.4. SMOOTHING SPLINES APPROACH        -   10.5. QUADRATIC MODEL APPROACH        -   10.6. LOCALLY WEIGHTED REGRESSION APPROACH        -   10.7. INTEGRATED MOVING AVERAGE APPROACH        -   10.8. RANDOM WALKS        -   10.9. MULTIVARIATE ADAPTIVE REGRESSION SPLINES MODEL    -   11. BENEFITS OF CERTAIN EMBODIMENTS

1. GENERAL OVERVIEW

Aspects of the disclosure generally relate to computer-implementedtechniques for determining characteristics of statistical datarepresenting crop yield data. The crop yield data usually includes dataprovided by agricultural statistics services. The data may be incompleteand for a variety of reasons may fail to capture characteristicsdetermined based on weather conditions, or soil cultivation techniques.For example, the data may not reflect interrelations in crop yieldmeasurements collected from neighboring fields, collected from theneighboring fields but at different weather conditions.

In an embodiment, a computer-implemented method allows determining thedata that is missing in crop yield reports and/or correcting data in thereports that was inaccurate. The method takes into consideration variousfactors that influence crop yield measurements. For example, theapproach allows taking into consideration any of inter-year yieldvariability, local weather conditions, or interrelations between theyields measured in adjacent fields.

The factors and the yield data may be represented using statisticalmodels. The models of crop yields may be verified against informationabout weather conditions, soil cultivation practices andharvest-specific anomalies.

Once one or more models are derived, the measurement data that issuspected of errors may be removed from the measurements dataset orcorrected in the dataset. Removing or correcting some of themeasurements helps reduce the impact on the yield measurements offactors such as weather.

2. 2. EXAMPLE AGRICULTURAL INTELLIGENCE COMPUTER SYSTEM

2.1. Structural Overview

FIG. 1 illustrates an example computer system that is configured toperform the functions described herein, shown in a field environmentwith other apparatus with which the system may interoperate. In oneembodiment, a user 102 owns, operates, or possesses a field managercomputing device 104 in a field location or associated with a fieldlocation such as a field intended for agricultural activities or amanagement location for one or more agricultural fields. The fieldmanager computing device 104 is programmed or configured to providefield data 106 to an agricultural intelligence computer system 130 viaone or more networks 109.

Examples of field data 106 include (a) identification data (for example,acreage, field name, field identifiers, geographic identifiers, boundaryidentifiers, crop identifiers, and any other suitable data that may beused to identify farm land, such as a common land unit (CLU), lot andblock number, a parcel number, geographic coordinates and boundaries,Farm Serial Number (FSN), farm number, tract number, field number,section, township, and/or range), (b) harvest data (for example, croptype, crop variety, crop rotation, whether the crop is grownorganically, harvest date, Actual Production History (APH), expectedyield, yield, crop price, crop revenue, grain moisture, tillagepractice, and previous growing season information), (c) soil data (forexample, type, composition, pH, organic matter (OM), cation exchangecapacity (CEC)), (d) planting data (for example, planting date, seed(s)type, relative maturity (RM) of planted seed(s), seed population), (e)fertilizer data (for example, nutrient type (Nitrogen, Phosphorous,Potassium), application type, application date, amount, source), (f)pesticide data (for example, pesticide, herbicide, fungicide, othersubstance or mixture of substances intended for use as a plantregulator, defoliant, or desiccant), (g) irrigation data (for example,application date, amount, source), (h) weather data (for example,precipitation, temperature, wind, forecast, pressure, visibility,clouds, heat index, dew point, humidity, snow depth, air quality,sunrise, sunset), (i) imagery data (for example, imagery and lightspectrum information from an agricultural apparatus sensor, camera,computer, smartphone, tablet, unmanned aerial vehicle, planes orsatellite), (j) scouting observations (photos, videos, free form notes,voice recordings, voice transcriptions, weather conditions (temperature,precipitation (current and over time), soil moisture, crop growth stage,wind velocity, relative humidity, dew point, black layer)), and (k)soil, seed, crop phenology, pest and disease reporting, and predictionssources and databases.

An external data server computer 108 is communicatively coupled toagricultural intelligence computer system 130 and is programmed orconfigured to send external data 110 to agricultural intelligencecomputer system 130 via the network(s) 109. The external data servercomputer 108 may be owned or operated by the same legal person or entityas the agricultural intelligence computer system 130, or by a differentperson or entity such as a government agency, non-governmentalorganization (NGO), and/or a private data service provider. Examples ofexternal data include weather data, imagery data, soil data, orstatistical data relating to crop yields, among others. External data110 may consist of the same type of information as field data 106. Insome embodiments, the external data 110 is provided by an external dataserver 108 owned by the same entity that owns and/or operates theagricultural intelligence computer system 130. For example, theagricultural intelligence computer system 130 may include a data serverfocused exclusively on a type of that might otherwise be obtained fromthird party sources, such as weather data.

An agricultural apparatus 111 has one or more remote sensors 112 fixedthereon, which sensors are communicatively coupled either directly orindirectly via agricultural apparatus 111 to the agriculturalintelligence computer system 130 and are programmed or configured tosend sensor data to agricultural intelligence computer system 130.Examples of agricultural apparatus 111 include tractors, combines,harvesters, planters, trucks, fertilizer equipment, unmanned aerialvehicles, and any other item of physical machinery or hardware,typically mobile machinery, and which may be used in tasks associatedwith agriculture. In some embodiments, a single unit of apparatus 111may comprise a plurality of sensors 112 that are coupled locally in anetwork on the apparatus; controller area network (CAN) is an example ofsuch a network that can be installed in combines or harvesters.Application controller 114 is communicatively coupled to agriculturalintelligence computer system 130 via the network(s) 109 and isprogrammed or configured to receive one or more scripts to control anoperating parameter of an agricultural vehicle or implement from theagricultural intelligence computer system 130. For instance, acontroller area network (CAN) bus interface may be used to enablecommunications from the agricultural intelligence computer system 130 tothe agricultural apparatus 111, such as how the CLIMATE FIELDVIEW DRIVE,available from The Climate Corporation, San Francisco, Calif., is used.Sensor data may consist of the same type of information as field data106.

The apparatus 111 may comprise a cab computer 115 that is programmedwith a cab application, which may comprise a version or variant of themobile application for device 104 that is further described in othersections herein. In an embodiment, cab computer 115 comprises a compactcomputer, often a tablet-sized computer or smartphone, with a colorgraphical screen display that is mounted within an operator's cab of theapparatus 111. Cab computer 115 may implement some or all of theoperations and functions that are described further herein for themobile computer device 104.

The network(s) 109 broadly represent any combination of one or more datacommunication networks including local area networks, wide areanetworks, internetworks or internets, using any of wireline or wirelesslinks, including terrestrial or satellite links. The network(s) may beimplemented by any medium or mechanism that provides for the exchange ofdata between the various elements of FIG. 1. The various elements ofFIG. 1 may also have direct (wired or wireless) communications links.The sensors 112, controller 114, external data server computer 108, andother elements of the system each comprise an interface compatible withthe network(s) 109 and are programmed or configured to use standardizedprotocols for communication across the networks such as TCP/IP, CANprotocol and higher-layer protocols such as HTTP, TLS, and the like.

Agricultural intelligence computer system 130 is programmed orconfigured to receive field data 106 from field manager computing device104, external data 110 from external data server computer 108, andsensor data from remote sensor 112. Agricultural intelligence computersystem 130 may be further configured to host, use or execute one or morecomputer programs, other software elements, digitally programmed logicsuch as FPGAs or ASICs, or any combination thereof to performtranslation and storage of data values, construction of digital modelsof one or more crops on one or more fields, generation ofrecommendations and notifications, and generation and sending of scriptsto application controller 114, in the manner described further in othersections of this disclosure.

In an embodiment, agricultural intelligence computer system 130 isprogrammed with or comprises a communication layer 132, presentationlayer 134, data management layer 140, hardware/virtualization layer 150,and model and field data repository 160. “Layer,” in this context,refers to any combination of electronic digital interface circuits,microcontrollers, firmware such as drivers, and/or computer programs orother software elements.

Communication layer 132 may be programmed or configured to performinput/output interfacing functions including sending requests to fieldmanager computing device 104, external data server computer 108, andremote sensor 112 for field data, external data, and sensor datarespectively. Communication layer 132 may be programmed or configured tosend the received data to model and field data repository 160 to bestored as field data 106.

In an embodiment, agricultural intelligence computer system 130 isprogrammed with or comprises code instructions 180. Code instructions180 may include one or more set of programming code instructions. Forexample, code instructions 180 may include data receiving instructions182 which, when executed by one or more processors, cause the processorsto perform receiving, over a computer network, electronic digital datacomprising first yield data representing crop yields harvested from anagricultural field. Code instructions 180 may also include passidentification instructions 187 which, when executed, cause identifyinga plurality of pass identifiers and a plurality of global positioningsystem times in the first yield data; filter outlier detectioninstructions 183 which, when executed by the processors, cause applyingone or more filters to the first yield data to identify, from the firstyield data, first outlier data. Furthermore, code instructions 180 mayinclude first stage filtering instructions 184 which, when executed bythe processors, cause generating first filtered data from the firstyield data by removing the first outlier data from the first yield data;spatial outlier detection instructions 185 which, when executed, causeidentifying, in the first filtered data, second outlier datarepresenting outlier values based on one or more outliercharacteristics; second stage filtering instructions 186 which, whenexecuted, cause generating second outlier data from the first filtereddata by removing the second outlier data from the first filtered data;and any other detection instructions 188.

Presentation layer 134 may be programmed or configured to generate agraphical user interface (GUI) to be displayed on field managercomputing device 104, cab computer 115 or other computers that arecoupled to the system 130 through the network 109. The GUI may comprisecontrols for inputting data to be sent to agricultural intelligencecomputer system 130, generating requests for models and/orrecommendations, and/or displaying recommendations, notifications,models, and other field data.

Data management layer 140 may be programmed or configured to manage readoperations and write operations involving the repository 160 and otherfunctional elements of the system, including queries and result setscommunicated between the functional elements of the system and therepository. Examples of data management layer 140 include JDBC, SQLserver interface code, and/or HADOOP interface code, among others.Repository 160 may comprise a database. As used herein, the term“database” may refer to either a body of data, a relational databasemanagement system (RDBMS), or to both. As used herein, a database maycomprise any collection of data including hierarchical databases,relational databases, flat file databases, object-relational databases,object oriented databases, and any other structured collection ofrecords or data that is stored in a computer system. Examples of RDBMS'sinclude, but are not limited to including, ORACLE®, MYSQL, IBM® DB2,MICROSOFT® SQL SERVER, SYBASE®, and POSTGRESQL databases. However, anydatabase may be used that enables the systems and methods describedherein.

When field data 106 is not provided directly to the agriculturalintelligence computer system via one or more agricultural machines oragricultural machine devices that interacts with the agriculturalintelligence computer system, the user 102 may be prompted via one ormore user interfaces on the user device (served by the agriculturalintelligence computer system) to input such information. In an exampleembodiment, the user 102 may specify identification data by accessing amap on the user device (served by the agricultural intelligence computersystem) and selecting specific CLUs that have been graphically shown onthe map. In an alternative embodiment, the user 102 may specifyidentification data by accessing a map on the user device (served by theagricultural intelligence computer system 130) and drawing boundaries ofthe field over the map. Such CLU selection or map drawings representgeographic identifiers. In alternative embodiments, the user 102 mayspecify identification data by accessing field identification data(provided as shape files or in a similar format) from the U. S.Department of Agriculture Farm Service Agency or other source via theuser device and providing such field identification data to theagricultural intelligence computer system.

In an embodiment, model and field data is stored in model and field datarepository 160. Model data comprises data models created for one or morefields. For example, a crop model may include a digitally constructedmodel of the development of a crop on the one or more fields. “Model,”in this context, refers to an electronic digitally stored set ofexecutable instructions and data values, associated with one another,which are capable of receiving and responding to a programmatic or otherdigital call, invocation, or request for resolution based upon specifiedinput values, to yield one or more stored output values that can serveas the basis of computer-implemented recommendations, output datadisplays, or machine control, among other things. Persons of skill inthe field find it convenient to express models using mathematicalequations, but that form of expression does not confine the modelsdisclosed herein to abstract concepts; instead, each model herein has apractical application in a computer in the form of stored executableinstructions and data that implement the model using the computer. Themodel data may include a model of past events on the one or more fields,a model of the current status of the one or more fields, and/or a modelof predicted events on the one or more fields. Model and field data maybe stored in data structures in memory, rows in a database table, inflat files or spreadsheets, or other forms of stored digital data.

Hardware/virtualization layer 150 comprises one or more centralprocessing units (CPUs), memory controllers, and other devices,components, or elements of a computer system such as volatile ornon-volatile memory, non-volatile storage such as disk, and I/O devicesor interfaces as illustrated and described, for example, in connectionwith FIG. 4. The layer 150 also may comprise programmed instructionsthat are configured to support virtualization, containerization, orother technologies.

For purposes of illustrating a clear example, FIG. 1 shows a limitednumber of instances of certain functional elements. However, in otherembodiments, there may be any number of such elements. For example,embodiments may use thousands or millions of different mobile computingdevices 104 associated with different users. Further, the system 130and/or external data server computer 108 may be implemented using two ormore processors, cores, clusters, or instances of physical machines orvirtual machines, configured in a discrete location or co-located withother elements in a datacenter, shared computing facility or cloudcomputing facility.

2.2. Application Program Overview

In an embodiment, the implementation of the functions described hereinusing one or more computer programs or other software elements that areloaded into and executed using one or more general-purpose computerswill cause the general-purpose computers to be configured as aparticular machine or as a computer that is specially adapted to performthe functions described herein. Further, each of the flow diagrams thatare described further herein may serve, alone or in combination with thedescriptions of processes and functions in prose herein, as algorithms,plans or directions that may be used to program a computer or logic toimplement the functions that are described. In other words, all theprose text herein, and all the drawing figures, together are intended toprovide disclosure of algorithms, plans or directions that aresufficient to permit a skilled person to program a computer to performthe functions that are described herein, in combination with the skilland knowledge of such a person given the level of skill that isappropriate for inventions and disclosures of this type.

In an embodiment, user 102 interacts with agricultural intelligencecomputer system 130 using field manager computing device 104 configuredwith an operating system and one or more application programs or apps;the field manager computing device 104 also may interoperate with theagricultural intelligence computer system 130 independently andautomatically under program control or logical control and direct userinteraction is not always required. Field manager computing device 104broadly represents one or more of a smart phone, PDA, tablet computingdevice, laptop computer, desktop computer, workstation, or any othercomputing device capable of transmitting and receiving information andperforming the functions described herein. Field manager computingdevice 104 may communicate via a network using a mobile applicationstored on field manager computing device 104, and in some embodiments,the device may be coupled using a cable 113 or connector to the sensor112 and/or controller 114. A particular user 102 may own, operate orpossess and use, in connection with system 130, more than one fieldmanager computing device 104 at a time.

The mobile application may provide client-side functionality, via thenetwork 109 to one or more mobile computing devices. In an exampleembodiment, field manager computing device 104 may access the mobileapplication via a web browser or a local client application or app.Field manager computing device 104 may transmit data to, and receivedata from, one or more front-end servers, using web-based protocols orformats such as HTTP, XML and/or JSON, or app-specific protocols. In anexample embodiment, the data may take the form of requests and userinformation input, such as field data, into the mobile computing device.In some embodiments, the mobile application interacts with locationtracking hardware and software on field manager computing device 104which determines the location of field manager computing device 104using standard tracking techniques such as multilateration of radiosignals, the global positioning system (GPS), Wi-Fi positioning systems,or other methods of mobile positioning. In some cases, location data orother data associated with the device 104, user 102, and/or useraccount(s) may be obtained by queries to an operating system of thedevice or by requesting an app on the device to obtain data from theoperating system.

In an embodiment, field manager computing device 104 sends field data106 to agricultural intelligence computer system 130 comprising orincluding data values representing one or more of: a geographicallocation of the one or more fields, tillage information for the one ormore fields, crops planted in the one or more fields, and soil dataextracted from the one or more fields. Field manager computing device104 may send field data 106 in response to user input from user 102specifying the data values for the one or more fields. Additionally,field manager computing device 104 may automatically send field data 106when one or more of the data values becomes available to field managercomputing device 104. For example, field manager computing device 104may be communicatively coupled to remote sensor 112 and/or applicationcontroller 114. In response to receiving data indicating thatapplication controller 114 released water onto the one or more fields,field manager computing device 104 may send field data 106 toagricultural intelligence computer system 130 indicating that water wasreleased on the one or more fields. Field data 106 identified in thisdisclosure may be input and communicated using electronic digital datathat is communicated between computing devices using parameterized URLsover HTTP, or another suitable communication or messaging protocol.

A commercial example of the mobile application is CLIMATE FIELDVIEW,commercially available from The Climate Corporation, San Francisco,Calif. The CLIMATE FIELDVIEW application, or other applications, may bemodified, extended, or adapted to include features, functions, andprogramming that have not been disclosed earlier than the filing date ofthis disclosure. In one embodiment, the mobile application comprises anintegrated software platform that allows a grower to make fact-baseddecisions for their operation because it combines historical data aboutthe grower's fields with any other data that the grower wishes tocompare. The combinations and comparisons may be performed in real timeand are based upon scientific models that provide potential scenarios topermit the grower to make better, more informed decisions.

FIG. 2 illustrates two views of an example logical organization of setsof instructions in main memory when an example mobile application isloaded for execution. In FIG. 2, each named element represents a regionof one or more pages of RAM or other main memory, or one or more blocksof disk storage or other non-volatile storage, and the programmedinstructions within those regions. In one embodiment, in view (a), amobile computer application 200 comprises account-fields-dataingestion-sharing instructions 202, overview and alert instructions 204,digital map book instructions 206, seeds and planting instructions 208,nitrogen instructions 210, weather instructions 212, field healthinstructions 214, and performance instructions 216.

In one embodiment, a mobile computer application 200 comprisingaccount-fields-data ingestion-sharing instructions 202 are programmed toreceive, translate, and ingest field data from third party systems viamanual upload or APIs. Data types may include field boundaries, yieldmaps, as-planted maps, soil test results, as-applied maps, and/ormanagement zones, among others. Data formats may include shape files,native data formats of third parties, and/or farm management informationsystem (FMIS) exports, among others. Receiving data may occur via manualupload, external APIs that push data to the mobile application, orinstructions that call APIs of external systems to pull data into themobile application.

In one embodiment, digital map book instructions 206 comprise field mapdata layers stored in device memory and are programmed with datavisualization tools and geospatial field notes. This provides growerswith convenient information close at hand for reference, logging andvisual insights into field performance. In one embodiment, overview andalert instructions 204 and programmed to provide an operation-wide viewof what is important to the grower, and timely recommendations to takeaction or focus on particular issues. This permits the grower to focustime on what needs attention, to save time and preserve yield throughoutthe season. In one embodiment, seeds and planting instructions 208 areprogrammed to provide tools for seed selection, hybrid placement, andscript creation, including variable rate (VR) script creation, basedupon scientific models and empirical data. This enables growers tomaximize yield or return on investment through optimized seed purchase,placement and population.

In one embodiment, nitrogen instructions 210 are programmed to providetools to inform nitrogen decisions by visualizing the availability ofnitrogen to crops and to create variable rate (VR) fertility scripts.This enables growers to maximize yield or return on investment throughoptimized nitrogen application during the season. Example programmedfunctions include displaying images such as SSURGO images to enabledrawing of application zones; upload of existing grower-defined zones;providing an application graph to enable tuning nitrogen applicationsacross multiple zones; output of scripts to drive machinery; tools formass data entry and adjustment; and/or maps for data visualization,among others. “Mass data entry,” in this context, may mean entering dataonce and then applying the same data to multiple fields that have beendefined in the system; example data may include nitrogen applicationdata that is the same for many fields of the same grower. For example,nitrogen instructions 210 may be programmed to accept definitions ofnitrogen planting and practices programs and to accept user inputspecifying to apply those programs across multiple fields. “Nitrogenplanting programs,” in this context, refers to a stored, named set ofdata that associates: a name, color code or other identifier, one ormore dates of application, types of material or product for each of thedates and amounts, method of application or incorporation such asinjected or knifed in, and/or amounts or rates of application for eachof the dates, crop or hybrid that is the subject of the application,among others. “Nitrogen practices programs,” in this context, refers toa stored, named set of data that associates: a practices name; aprevious crop; a tillage system; a date of primarily tillage; one ormore previous tillage systems that were used; one or more indicators ofmanure application that were used. Nitrogen instructions 210 also may beprogrammed to generate and cause displaying a nitrogen graph, once aprogram is applied to a field, which indicates projections of plant useof the specified nitrogen and whether a surplus or shortfall ispredicted; in some embodiments, different color indicators may signal amagnitude of surplus or magnitude of shortfall. In one embodiment, anitrogen graph comprises a graphical display in a computer displaydevice comprising a plurality of rows, each row associated with andidentifying a field; data specifying what crop is planted in the field,the field size, the field location, and a graphic representation of thefield perimeter; in each row, a timeline by month with graphicindicators specifying each nitrogen application and amount at pointscorrelated to month names; and numeric and/or colored indicators ofsurplus or shortfall, in which color indicates magnitude.

In one embodiment, weather instructions 212 are programmed to providefield-specific recent weather data and forecasted weather information.This enables growers to save time and have an efficient integrateddisplay with respect to daily operational decisions.

In one embodiment, field health instructions 214 are programmed toprovide timely remote sensing images highlighting in-season cropvariation and potential concerns. Example programmed functions includecloud checking, to identify possible clouds or cloud shadows;determining nitrogen indices based on field images; graphicalvisualization of scouting layers, including, for example, those relatedto field health, and viewing and/or sharing of scouting notes; and/ordownloading satellite images from multiple sources and prioritizing theimages for the grower, among others.

In one embodiment, performance instructions 216 are programmed toprovide reports, analysis, and insight tools using on-farm data forevaluation, insights and decisions. This enables the grower to seekimproved outcomes for the next year through fact-based conclusions aboutwhy return on investment was at prior levels, and insight intoyield-limiting factors. The performance instructions 216 may beprogrammed to communicate via the network(s) 109 to back-end analyticsprograms executed at external data server computer 108 and configured toanalyze metrics such as yield, hybrid, population, SSURGO, soil tests,or elevation, among others. Programmed reports and analysis may includeyield variability analysis, benchmarking of yield and other metricsagainst other growers based on anonymized data collected from manygrowers, or data for seeds and planting, among others.

Applications having instructions configured in this way may beimplemented for different computing device platforms while retaining thesame general user interface appearance. For example, the mobileapplication may be programmed for execution on tablets, smartphones, orserver computers that are accessed using browsers at client computers.Further, the mobile application as configured for tablet computers orsmartphones may provide a full app experience or a cab app experiencethat is suitable for the display and processing capabilities of cabcomputer 115. For example, referring now to view (b) of FIG. 2, in oneembodiment a cab computer application 220 may comprise maps-cabinstructions 222, remote view instructions 224, data collect andtransfer instructions 226, machine alerts instructions 228, scripttransfer instructions 230, and scouting-cab instructions 232. The codebase for the instructions of view (b) may be the same as for view (a)and executables implementing the code may be programmed to detect thetype of platform on which they are executing and to expose, through agraphical user interface, only those functions that are appropriate to acab platform or full platform. This approach enables the system torecognize the distinctly different user experience that is appropriatefor an in-cab environment and the different technology environment ofthe cab. The maps-cab instructions 222 may be programmed to provide mapviews of fields, farms or regions that are useful in directing machineoperation. The remote view instructions 224 may be programmed to turnon, manage, and provide views of machine activity in real-time or nearreal-time to other computing devices connected to the system 130 viawireless networks, wired connectors or adapters, and the like. The datacollect and transfer instructions 226 may be programmed to turn on,manage, and provide transfer of data collected at machine sensors andcontrollers to the system 130 via wireless networks, wired connectors oradapters, and the like. The machine alerts instructions 228 may beprogrammed to detect issues with operations of the machine or tools thatare associated with the cab and generate operator alerts. The scripttransfer instructions 230 may be configured to transfer in scripts ofinstructions that are configured to direct machine operations or thecollection of data. The scouting-cab instructions 232 may be programmedto display location-based alerts and information received from thesystem 130 based on the location of the agricultural apparatus 111 orsensors 112 in the field and ingest, manage, and provide transfer oflocation-based scouting observations to the system 130 based on thelocation of the agricultural apparatus 111 or sensors 112 in the field.

2.3. Data Ingest to the Computer System

In an embodiment, external data server computer 108 stores external data110, including soil data representing soil composition for the one ormore fields and weather data representing temperature and precipitationon the one or more fields. The weather data may include past and presentweather data as well as forecasts for future weather data. In anembodiment, external data server computer 108 comprises a plurality ofservers hosted by different entities. For example, a first server maycontain soil composition data while a second server may include weatherdata. Additionally, soil composition data may be stored in multipleservers. For example, one server may store data representing percentageof sand, silt, and clay in the soil while a second server may store datarepresenting percentage of organic matter (OM) in the soil.

In an embodiment, remote sensor 112 comprises one or more sensors thatare programmed or configured to produce one or more observations. Remotesensor 112 may be aerial sensors, such as satellites, vehicle sensors,planting equipment sensors, tillage sensors, fertilizer or insecticideapplication sensors, harvester sensors, and any other implement capableof receiving data from the one or more fields. In an embodiment,application controller 114 is programmed or configured to receiveinstructions from agricultural intelligence computer system 130.Application controller 114 may also be programmed or configured tocontrol an operating parameter of an agricultural vehicle or implement.For example, an application controller may be programmed or configuredto control an operating parameter of a vehicle, such as a tractor,planting equipment, tillage equipment, fertilizer or insecticideequipment, harvester equipment, or other farm implements such as a watervalve. Other embodiments may use any combination of sensors andcontrollers, of which the following are merely selected examples.

The system 130 may obtain or ingest data under user 102 control, on amass basis from a large number of growers who have contributed data to ashared database system. This form of obtaining data may be termed“manual data ingest” as one or more user-controlled computer operationsare requested or triggered to obtain data for use by the system 130. Asan example, the NITROGEN ADVISOR, commercially available from TheClimate Corporation, San Francisco, Calif., may be operated to exportdata to system 130 for storing in the repository 160.

For example, seed monitor systems can both control planter apparatuscomponents and obtain planting data, including signals from seed sensorsvia a signal harness that comprises a CAN backbone and point-to-pointconnections for registration and/or diagnostics. Seed monitor systemscan be programmed or configured to display seed spacing, population andother information to the user via the cab computer 115 or other deviceswithin the system 130. Examples are disclosed in U.S. Pat. No. 8,738,243and U.S. Pat. Pub. 2015/0094916, and the present disclosure assumesknowledge of those other patent disclosures.

Likewise, yield monitor systems may contain yield sensors for harvesterapparatus that send yield measurement data to the cab computer 115 orother devices within the system 130. Yield monitor systems may utilizeone or more remote sensors 112 to obtain grain moisture measurements ina combine or other harvester and transmit these measurements to the uservia the cab computer 115 or other devices within the system 130.

In an embodiment, examples of sensors 112 that may be used with anymoving vehicle or apparatus of the type described elsewhere hereininclude kinematic sensors and position sensors. Kinematic sensors maycomprise any of speed sensors such as radar or wheel speed sensors,accelerometers, or gyros. Position sensors may comprise GPS receivers ortransceivers, or Wi-Fi-based position or mapping apps that areprogrammed to determine location based upon nearby Wi-Fi hotspots, amongothers.

In an embodiment, examples of sensors 112 that may be used with tractorsor other moving vehicles include engine speed sensors, fuel consumptionsensors, area counters or distance counters that interact with GPS orradar signals, PTO (power take-off) speed sensors, tractor hydraulicssensors configured to detect hydraulics parameters such as pressure orflow, and/or and hydraulic pump speed, wheel speed sensors or wheelslippage sensors. In an embodiment, examples of controllers 114 that maybe used with tractors include hydraulic directional controllers,pressure controllers, and/or flow controllers; hydraulic pump speedcontrollers; speed controllers or governors; hitch position controllers;or wheel position controllers provide automatic steering.

In an embodiment, examples of sensors 112 that may be used with seedplanting equipment such as planters, drills, or air seeders include seedsensors, which may be optical, electromagnetic, or impact sensors;downforce sensors such as load pins, load cells, pressure sensors; soilproperty sensors such as reflectivity sensors, moisture sensors,electrical conductivity sensors, optical residue sensors, or temperaturesensors; component operating criteria sensors such as planting depthsensors, downforce cylinder pressure sensors, seed disc speed sensors,seed drive motor encoders, seed conveyor system speed sensors, or vacuumlevel sensors; or pesticide application sensors such as optical or otherelectromagnetic sensors, or impact sensors. In an embodiment, examplesof controllers 114 that may be used with such seed planting equipmentinclude: toolbar fold controllers, such as controllers for valvesassociated with hydraulic cylinders; downforce controllers, such ascontrollers for valves associated with pneumatic cylinders, airbags, orhydraulic cylinders, and programmed for applying downforce to individualrow units or an entire planter frame; planting depth controllers, suchas linear actuators; metering controllers, such as electric seed meterdrive motors, hydraulic seed meter drive motors, or swath controlclutches; hybrid selection controllers, such as seed meter drive motors,or other actuators programmed for selectively allowing or preventingseed or an air-seed mixture from delivering seed to or from seed metersor central bulk hoppers; metering controllers, such as electric seedmeter drive motors, or hydraulic seed meter drive motors; seed conveyorsystem controllers, such as controllers for a belt seed deliveryconveyor motor; marker controllers, such as a controller for a pneumaticor hydraulic actuator; or pesticide application rate controllers, suchas metering drive controllers, orifice size or position controllers.

In an embodiment, examples of sensors 112 that may be used with tillageequipment include position sensors for tools such as shanks or discs;tool position sensors for such tools that are configured to detectdepth, gang angle, or lateral spacing; downforce sensors; or draft forcesensors. In an embodiment, examples of controllers 114 that may be usedwith tillage equipment include downforce controllers or tool positioncontrollers, such as controllers configured to control tool depth, gangangle, or lateral spacing.

In an embodiment, examples of sensors 112 that may be used in relationto apparatus for applying fertilizer, insecticide, fungicide and thelike, such as on-planter starter fertilizer systems, subsoil fertilizerapplicators, fertilizer sprayers, or irrigation systems, include: fluidsystem criteria sensors, such as flow sensors or pressure sensors;sensors indicating which spray head valves or fluid line valves areopen; sensors associated with tanks, such as fill level sensors;sectional or system-wide supply line sensors, or row-specific supplyline sensors; or kinematic sensors such as accelerometers disposed onsprayer booms. In an embodiment, examples of controllers 114 that may beused with such apparatus include pump speed controllers; valvecontrollers that are programmed to control pressure, flow, direction,PWM and the like; or position actuators, such as for boom height,subsoiler depth, or boom position.

In an embodiment, examples of sensors 112 that may be used withharvesters include yield monitors, such as impact plate strain gauges orposition sensors, capacitive flow sensors, load sensors, weight sensors,or torque sensors associated with elevators or augers, or optical orother electromagnetic grain height sensors; grain moisture sensors, suchas capacitive sensors; grain loss sensors, including impact, optical, orcapacitive sensors; header operating criteria sensors such as headerheight, header type, deck plate gap, feeder speed, and reel speedsensors; separator operating criteria sensors, such as concaveclearance, rotor speed, shoe clearance, or chaffer clearance sensors;auger sensors for position, operation, or speed; or engine speedsensors. In an embodiment, examples of controllers 114 that may be usedwith harvesters include header operating criteria controllers forelements such as header height, header type, deck plate gap, feederspeed, or reel speed; separator operating criteria controllers forfeatures such as concave clearance, rotor speed, shoe clearance, orchaffer clearance; or controllers for auger position, operation, orspeed.

In an embodiment, examples of sensors 112 that may be used with graincarts include weight sensors, or sensors for auger position, operation,or speed. In an embodiment, examples of controllers 114 that may be usedwith grain carts include controllers for auger position, operation, orspeed.

In an embodiment, examples of sensors 112 and controllers 114 may beinstalled in unmanned aerial vehicle (UAV) apparatus or “drones.” Suchsensors may include cameras with detectors effective for any range ofthe electromagnetic spectrum including visible light, infrared,ultraviolet, near-infrared (NIR), and the like; accelerometers;altimeters; temperature sensors; humidity sensors; pitot tube sensors orother airspeed or wind velocity sensors; battery life sensors; or radaremitters and reflected radar energy detection apparatus. Suchcontrollers may include guidance or motor control apparatus, controlsurface controllers, camera controllers, or controllers programmed toturn on, operate, obtain data from, manage and configure any of theforegoing sensors. Examples are disclosed in U.S. patent applicationSer. No. 14/831,165 and the present disclosure assumes knowledge of thatother patent disclosure.

In an embodiment, sensors 112 and controllers 114 may be affixed to soilsampling and measurement apparatus that is configured or programmed tosample soil and perform soil chemistry tests, soil moisture tests, andother tests pertaining to soil. For example, the apparatus disclosed inU.S. Pat. Nos. 8,767,194 and 8,712,148 may be used, and the presentdisclosure assumes knowledge of those patent disclosures.

2.4. Process Overview—Agronomic Model Training

In an embodiment, the agricultural intelligence computer system 130 isprogrammed or configured to create an agronomic model. In this context,an agronomic model is a data structure in memory of the agriculturalintelligence computer system 130 that comprises field data 106, such asidentification data and harvest data for one or more fields. Theagronomic model may also comprise calculated agronomic properties whichdescribe either conditions which may affect the growth of one or morecrops on a field, or properties of the one or more crops, or both.Additionally, an agronomic model may comprise recommendations based onagronomic factors such as crop recommendations, irrigationrecommendations, planting recommendations, and harvestingrecommendations. The agronomic factors may also be used to estimate oneor more crop related results, such as agronomic yield. The agronomicyield of a crop is an estimate of quantity of the crop that is produced,or in some examples the revenue or profit obtained from the producedcrop.

In an embodiment, the agricultural intelligence computer system 130 mayuse a preconfigured agronomic model to calculate agronomic propertiesrelated to currently received location and crop information for one ormore fields. The preconfigured agronomic model is based upon previouslyprocessed field data, including but not limited to, identification data,harvest data, fertilizer data, and weather data. The preconfiguredagronomic model may have been cross validated to ensure accuracy of themodel. Cross validation may include comparison to ground truthing thatcompares predicted results with actual results on a field, such as acomparison of precipitation estimate with a rain gauge at the samelocation or an estimate of nitrogen content with a soil samplemeasurement.

FIG. 3 illustrates a programmed process by which the agriculturalintelligence computer system generates one or more preconfiguredagronomic models using field data provided by one or more external datasources. FIG. 3 may serve as an algorithm or instructions forprogramming the functional elements of the agricultural intelligencecomputer system 130 to perform the operations that are now described.

At block 305, the agricultural intelligence computer system 130 isconfigured or programmed to implement agronomic data preprocessing offield data received from one or more external data resources. The fielddata received from one or more external data resources may bepreprocessed for the purpose of removing noise and distorting effectswithin the agronomic data including measured outliers that would biasreceived field data values. Embodiments of agronomic data preprocessingmay include, but are not limited to, removing data values commonlyassociated with outlier data values, specific measured data points thatare known to unnecessarily skew other data values, data smoothingtechniques used to remove or reduce additive or multiplicative effectsfrom noise, and other filtering or data derivation techniques used toprovide clear distinctions between positive and negative data inputs.Various embodiments of these techniques include, but are not limited to,those described herein.

At block 310, the agricultural intelligence computer system 130 isconfigured or programmed to perform data subset selection using thepreprocessed field data in order to identify datasets useful for initialagronomic model generation. The agricultural intelligence computersystem 130 may implement data subset selection techniques including, butnot limited to, a genetic algorithm method, an all subset models method,a sequential search method, a stepwise regression method, a particleswarm optimization method, and an ant colony optimization method. Forexample, a genetic algorithm selection technique uses an adaptiveheuristic search algorithm, based on evolutionary principles of naturalselection and genetics, to determine and evaluate datasets within thepreprocessed agronomic data.

At block 315, the agricultural intelligence computer system 130 isconfigured or programmed to implement field dataset evaluation. In anembodiment, a specific field dataset is evaluated by creating anagronomic model and using specific quality thresholds for the createdagronomic model. Agronomic models may be compared using cross validationtechniques including, but not limited to, root mean square error ofleave-one-out cross validation (RMSECV), mean absolute error, and meanpercentage error. For example, RMSECV can cross validate agronomicmodels by comparing predicted agronomic property values created by theagronomic model against historical agronomic property values collectedand analyzed. In an embodiment, the agronomic dataset evaluation logicis used as a feedback loop where agronomic datasets that do not meetconfigured quality thresholds are used during future data subsetselection steps (block 310).

At block 320, the agricultural intelligence computer system 130 isconfigured or programmed to implement agronomic model creation basedupon the cross validated agronomic datasets. In an embodiment, agronomicmodel creation may implement multivariate regression techniques tocreate preconfigured agronomic data models.

At block 325, the agricultural intelligence computer system 130 isconfigured or programmed to store the preconfigured agronomic datamodels for future field data evaluation.

2.5. Implementation Example—Hardware Overview

According to one embodiment, the techniques described herein areimplemented by one or more special-purpose computing devices. Thespecial-purpose computing devices may be hard-wired to perform thetechniques, or may include digital electronic devices such as one ormore application-specific integrated circuits (ASICs) or fieldprogrammable gate arrays (FPGAs) that are persistently programmed toperform the techniques, or may include one or more general purposehardware processors programmed to perform the techniques pursuant toprogram instructions in firmware, memory, other storage, or acombination. Such special-purpose computing devices may also combinecustom hard-wired logic, ASICs, or FPGAs with custom programming toaccomplish the techniques. The special-purpose computing devices may bedesktop computer systems, portable computer systems, handheld devices,networking devices or any other device that incorporates hard-wiredand/or program logic to implement the techniques.

For example, FIG. 4 is a block diagram that illustrates a computersystem 400 upon which an embodiment of the invention may be implemented.Computer system 400 includes a bus 402 or other communication mechanismfor communicating information, and a hardware processor 404 coupled withbus 402 for processing information. Hardware processor 404 may be, forexample, a general purpose microprocessor.

Computer system 400 also includes a main memory 406, such as a randomaccess memory (RAM) or other dynamic storage device, coupled to bus 402for storing information and instructions to be executed by processor404. Main memory 406 also may be used for storing temporary variables orother intermediate information during execution of instructions to beexecuted by processor 404. Such instructions, when stored innon-transitory storage media accessible to processor 404, rendercomputer system 400 into a special-purpose machine that is customized toperform the operations specified in the instructions.

Computer system 400 further includes a read only memory (ROM) 408 orother static storage device coupled to bus 402 for storing staticinformation and instructions for processor 404. A storage device 410,such as a magnetic disk, optical disk, or solid-state drive is providedand coupled to bus 402 for storing information and instructions.

Computer system 400 may be coupled via bus 402 to a display 412, such asa cathode ray tube (CRT), for displaying information to a computer user.An input device 414, including alphanumeric and other keys, is coupledto bus 402 for communicating information and command selections toprocessor 404. Another type of user input device is cursor control 416,such as a mouse, a trackball, or cursor direction keys for communicatingdirection information and command selections to processor 404 and forcontrolling cursor movement on display 412. This input device typicallyhas two degrees of freedom in two axes, a first axis (e.g., x) and asecond axis (e.g., y), that allows the device to specify positions in aplane.

Computer system 400 may implement the techniques described herein usingcustomized hard-wired logic, one or more ASICs or FPGAs, firmware and/orprogram logic which in combination with the computer system causes orprograms computer system 400 to be a special-purpose machine. Accordingto one embodiment, the techniques herein are performed by computersystem 400 in response to processor 404 executing one or more sequencesof one or more instructions contained in main memory 406. Suchinstructions may be read into main memory 406 from another storagemedium, such as storage device 410. Execution of the sequences ofinstructions contained in main memory 406 causes processor 404 toperform the process steps described herein. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions.

The term “storage media” as used herein refers to any non-transitorymedia that store data and/or instructions that cause a machine tooperate in a specific fashion. Such storage media may comprisenon-volatile media and/or volatile media. Non-volatile media includes,for example, optical disks, magnetic disks, or solid-state drives, suchas storage device 410. Volatile media includes dynamic memory, such asmain memory 406. Common forms of storage media include, for example, afloppy disk, a flexible disk, hard disk, solid-state drive, magnetictape, or any other magnetic data storage medium, a CD-ROM, any otheroptical data storage medium, any physical medium with patterns of holes,a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip orcartridge.

Storage media is distinct from but may be used in conjunction withtransmission media. Transmission media participates in transferringinformation between storage media. For example, transmission mediaincludes coaxial cables, copper wire and fiber optics, including thewires that comprise bus 402. Transmission media can also take the formof acoustic or light waves, such as those generated during radio-waveand infra-red data communications.

Various forms of media may be involved in carrying one or more sequencesof one or more instructions to processor 404 for execution. For example,the instructions may initially be carried on a magnetic disk orsolid-state drive of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 400 canreceive the data on the telephone line and use an infra-red transmitterto convert the data to an infra-red signal. An infra-red detector canreceive the data carried in the infra-red signal and appropriatecircuitry can place the data on bus 402. Bus 402 carries the data tomain memory 406, from which processor 404 retrieves and executes theinstructions. The instructions received by main memory 406 mayoptionally be stored on storage device 410 either before or afterexecution by processor 404.

Computer system 400 also includes a communication interface 418 coupledto bus 402. Communication interface 418 provides a two-way datacommunication coupling to a network link 420 that is connected to alocal network 422. For example, communication interface 418 may be anintegrated services digital network (ISDN) card, cable modem, satellitemodem, or a modem to provide a data communication connection to acorresponding type of telephone line. As another example, communicationinterface 418 may be a local area network (LAN) card to provide a datacommunication connection to a compatible LAN. Wireless links may also beimplemented. In any such implementation, communication interface 418sends and receives electrical, electromagnetic or optical signals thatcarry digital data streams representing various types of information.

Network link 420 typically provides data communication through one ormore networks to other data devices. For example, network link 420 mayprovide a connection through local network 422 to a host computer 424 orto data equipment operated by an Internet Service Provider (ISP) 426.ISP 426 in turn provides data communication services through the worldwide packet data communication network now commonly referred to as the“Internet” 428. Local network 422 and Internet 428 both use electrical,electromagnetic or optical signals that carry digital data streams. Thesignals through the various networks and the signals on network link 420and through communication interface 418, which carry the digital data toand from computer system 400, are example forms of transmission media.

Computer system 400 can send messages and receive data, includingprogram code, through the network(s), network link 420 and communicationinterface 418. In the Internet example, a server 430 might transmit arequested code for an application program through Internet 428, ISP 426,local network 422 and communication interface 418.

The received code may be executed by processor 404 as it is received,and/or stored in storage device 410, or other non-volatile storage forlater execution.

3. CHARACTERISTICS OF MEASUREMENTS OF YIELDS OF CROPS

In an embodiment, yield measurements of crops harvested fromagricultural fields depend on characteristics of the fields from whichthe crops are harvested. These characteristics may depend on the type ofsoil in the fields, the effectiveness of soil cultivation, theeffectiveness of soil fertilization, or the quality of seeds used toproduce the crops.

Values associated with some of the characteristics may increase overtime, especially if the process of fertilizing of the soil improves, orbetter nutrients are provided to the soil, or better seeds are used toproduce the crops. These characteristics may be modeled as constantvariables in a mathematical model that represents yield measurements.The representations of these characteristics may be removed from theequations used to approximate or predict yield values that are missingin the received yield measurements.

Other characteristics may be stochastic and may depend, for example, onweather conditions. Since weather conditions are not perfectlypredictable, a model to represent yield measurements and stochasticcharacteristics may use variables that are associated with values thatmay change frequently. These variables are not removed from modelequations, as weather-dependent characteristics may significantlyinfluence the process of approximating or predicting crop yield values.

4. MODELING TRENDS IN CROP YIELDS

In an embodiment, a computer-implemented approach takes intoconsideration data values that have been provided as measurements orobservations. The measurements may be collected from agricultural fieldsduring activities such as crop harvesting. For example, the measurementsmay include data values representing yields of crops harvested by one ormore combine harvesters from one or more agricultural fields and at twoor more time points.

The modeling of the data trends may include forecasting data valuesrepresenting future crop yields. The forecasting may be performed basedon factors such as yield variability, local weather conditions, orinterrelations between the crop yields measured in adjacent fields. Theapproach may also include reconstructing values for the missing data,and correcting inaccurate data. Trend reports may be generated based onthe corrected measurements.

4.1. Models for Representing Yields of Crops

A process of forecasting and/or reconstructing yield data may be termeddetrending the measurements, or detrending a yield data model. Adetrending model can be interpreted as a model in which the crop yieldmeasurements have been modified by taking into consideration localweather conditions or soil characteristics.

Models of crop yield data may be verified against information aboutlocal weather conditions, yield measurements, and other inputs that insome manner contribute to anomalies occurring in the crop yields. Theadditional information, including the weather-based-inputs, may be usedin a process of detrending the yield anomaly in the models. Theresulting models may be subjected to a comparative assessment of severaldifferent detrending methods and comparison charts of the differentdetrending approaches may be generated.

4.2. Linear Model

A linear detrending model may be generated based on the reported yieldvalues for crops collected from one or more regions and collected duringtwo or more time periods or at two or more time points. Since therelation between the yields collected from the regions and over theyears is usually linear, the most applicable models are the models thatuse linear formulas.

In an embodiment, linear model M derived for observations of yieldsy_(i,t) observed in a county or other region i in a year t may becomputed using the following formula:

y _(i,t) =M(i,t)+ε_(i,t)  (1)

where ε_((i,t)) is an error value computed for a county or other regioni and a year t.

The prediction error term ε_((i,t)) may be a function of aregion-based-parameter and/or a time-based-parameter. The predictionerror E may be interpreted as a yield anomaly. For example, theprediction error ε_((i,t)) may be interpreted as a yield anomaly thatoccurred in the i-th region and the t-th year.

In an embodiment, performance of a model M (i, t) may be measured byfitting the model M (i, t) with observations y_(i,t) for t∈[a, b] andevaluating an absolute error |ε| for predictions y_(i, b+1) in the yearfollowing the training period. The [a, b] is a range of years. Forexample, a one-year-ahead prediction may be computed using theobservations over a range of years and any of the mean absolute error(MAE) approaches known in statistics.

Model performance may be evaluated using a function computed for theobservations y_(i,t) for t∈[a, b] representing a range of years duringwhich the model was trained. Once the model M (i, t) is trained on oneor more sets of training data, the model M (i, t) may be used to verifyan accuracy and/or completeness of observations y_(i,t) for t∈[a, b],where [a, b] is a range of years.

Various classes of models have been applied to determine approximatevalues in a crop yield data set. Examples of the applied models are themodels that assume either normally distributed errors or non-normalerrors. Examples of several models are described below.

In an embodiment, a class of models M (i, t) may be represented usingthe following equation:

y _(i,t)=α_(i) t+β _(i)=ε_(i,t)  (2)

where y_(i,t) corresponds to crop yield measurements for the cropsharvested from an i-th field and a t-th time point; α_(i) corresponds toa linear model variable that depends at least on a time parameter; β_(i)corresponds to a vertical offset value for the linear model; and ε_(i,t)corresponds to an error term.

In the class of linear models y_((i,t)), the term α_(i) represents atime-dependent-coefficient determined for the i-th county, whereas β_(i)represents a time-independent-coefficient for the i-th county. The termsα_(i) and β_(i) may be identical (α_(i)=α for all i, and β_(i)=β for alli). The class of linear models y_(i,t) may have some additionalstatistical structures reflecting the dependence of the coefficient onthe time-parameters and/or the county-parameter. The models y_(i,t) maybe used to verify an accuracy and/or completeness of observationsy_(i,t) for t∈[a, b], where [a, b] is a range of years, and if needed todetermine missing observations and/or to correct the observations thatoriginally contained errors.

The linear function y_((i,t)) representing crop yield measurements, maybe graphically represented using a two dimensional graph.

5. SINGULAR VALUE DECOMPOSITION

A singular value decomposition (SVD) approach is typically used toreduce an input dataset. More specifically, SVD is usually used toreduce an input dataset containing a large number of values to an outputdataset containing significantly fewer values which, however, at leastpartially capture the variability of the values in the input dataset.Obtaining the output dataset with significantly fewer values than in theinput dataset allows conserving computational and storage resourceswhile maintaining the data that captures a large fraction of thevariability that is present in the original data.

SVD is particularly useful in the analysis of data representing yield ofcrops harvested from agricultural fields. Crop yield data in theagricultural and farming sciences often include very large datasets, andprocessing the very large datasets usually puts significant demands oncomputational and storage resources of computational systems. However,if the yield datasets are decomposed to smaller datasets of values whichexhibit large spatial correlations, the decomposed small dataset may beused to approximate the crop yield datasets to some degree.

In an embodiment, SVD analysis may result in a relatively compactrepresentation of the correlations exhibited by the data in the cropyield datasets, especially if the datasets are multivariate datasets.Furthermore, SVD analysis may provide insight into spatial and temporalvariations exhibited in the analyzed crop yield data.

A set of data analyzed using SVD may exhibit anomalies and/or errors,but may also capture spatial dependencies between the data items and/orat least some of the subset of the data items. Examples of spatialdependencies may include spatial relationships between the fields andsub-fields for which the subset of the data is collected, spatialrelationships between time components in the data subsets, and the like.

The set of data analyzed using SVD may be used to de-trend the raw yieldmeasurements. Since the purpose of SVD is to find spatial correlationsindependent of trends, the measurement data may be de-trended byperforming SVD analysis. For example, a very large data set of yields ofcrop measurements may be first decomposed into decomposed datasets, thenthe decomposed datasets may be used to generate a reconstructed dataset,and the reconstructed dataset may be used to represent the de-trendeddata.

In an embodiment, SVD analysis is applied to determine and analyzespatial and temporal relations in the yield of crops collected fromagricultural fields. SVD analysis may be applied to a set ofobservations representing crop yield data having associated informationabout the locations from which the crops were harvested and havingassociated information about the times at which the crops wereharvested. For example, the crop yield data may represent cropsharvested from a plurality of locations and at a plurality of timepoints.

In an embodiment, crop yield data may be represented using atwo-dimensional matrix capturing crop yield observations recorded at aplurality of time points and from a plurality of agricultural fields.The fields are often referred to as locations, and each of the pluralityof locations may be identified in the matrix by a location identifier,or the like. The plurality of time points may be used to determine atemporal resolution with which the crops were harvested. A temporalresolution may depend on various factors, such as a total time duringwhich the measurements were collected, characteristics of the crop yieldmeasuring sensors and devices, precision and calibration of the sensorsand devices, and the like.

SVD approach may be used to forecast future time yield data and/or toreconstruct the yield data received from reporting agencies. Thereceived yield data may be decomposed, and the decomposed data may beused to generate forecasted future time yield data or reconstruct pastor present time yield data. For example, a particular future time yielddata item may be computed from the decomposed yield data, andincorporated into the yield data set.

In an embodiment, one or more decomposed yield data items may begenerated using regional yield trend data that is specific to one ormore fields of the plurality of agricultural fields and national yieldtrend data. The one or more decomposed yield data items may also begenerated using one or more weather variability components and one ormore weather patterns that are specific to the plurality of agriculturalfields.

One or more decomposed yield data items may be used to generate one ormore particular yield data items. The one or more particular yield dataitems may be used to generate forecasted yield data and/or toreconstruct data in a matrix containing yield data representing theyields of crops harvested from agricultural fields. Forecasting orreconstructing yield data may be performed by incorporating the one ormore particular yield data into the yield data.

One or more particular yield data may also be used to modify one or moreoriginal yield data items that have been determined to be inaccurate.For example, the one or more particular yield data may be used toreplace the respective inaccurate original yield data items in a matrixrepresenting the crop measurements.

Decomposed yield data and reconstructed yield data may be used tointerpret spatial and temporal dependencies between the yields of thecrops that were harvested from the plurality of agricultural fields andto determine how weather conditions and region adjacencies influence theyields. For example, the decomposed yield data and the reconstructedyield data may indicate that the yields of crops harvested from theneighboring fields and approximately within the same month of the yearare expected to be similar, while the yields of corps harvested fromremote fields and at the time intervals that are far apart from eachother are expected to be dissimilar.

In an embodiment, yield data received in a set of measurementsrepresenting yields of crops harvested from agricultural fields may beused to generate a model of the yield data. The model may be modified,reconstructed, or otherwise augmented. The model may be implemented asany type of data model. Examples of the models include a mean valuemodel, a linear model, a robust linear model, a smooth spline model, aquadratic model, a locally weighted regression model, an integratedmoving average model, a random walk model, a multivariate adaptiveregression splines model, any type of SVD model, a conditionalautoregressive (CAR) model, and the like.

SVD approach may be applied to a two-dimensional matrix capturing cropyield observations. Assuming the closest rank-p approximation, atwo-dimensional matrix X may be expressed as a product of threematrices: an orthogonal matrix U, a diagonal matrix S, and a transposeof an orthogonal matrix V. Hence, a crop yield data set X may bedecomposed into USV^(t) and expressed as:

$\begin{matrix}{\quad\begin{matrix}{X = {USV}^{t}} \\{= {\sum\limits_{k = 1}^{p}{s_{k}u_{k}v_{k}^{t}}}}\end{matrix}} & (3)\end{matrix}$

FIG. 6 depicts a block diagram that depicts an example SVD approach fordecomposing a large set of crop yield values into smaller subsets thatapproximate the large set of crop yield values. In an embodiment, alarge set of crop yield values representing crop yields harvested from aplurality of locations l=1, . . . , and harvested at times t=1, . . . ,may be stored in a two dimensional table 110.

In table 110, rows may correspond to locations 1 of agricultural fields,while columns may correspond to time points t at which the measurementswere collected. In the example depicted in FIG. 6, table 110 is indexedusing a time horizontal axis 114 and a location vertical axis 118.Horizontal axis 114 may correspond to a time axis, while vertical axis118 may correspond to a location axis. A data value stored in table 110at the intersection of a particular row and a particular columncorresponds to raw yield measurement data representing crop yieldcollected from a particular field identified by a location identifierl_(r) and a time point identifier t_(s). In the example depicted in FIG.6, three crop yield values 150 may correspond to crop yield valuescollected from location 118 at three consecutive time points 115, 116,117.

Generating SVD decomposition may start with determining a rankapproximation constant, which may depend on a vertical dimension of thetable X110. In particular, a closest rank-p approximation may bedetermined. The table X110 may be decomposed and expressed as a productof three matrices: an orthogonal matrix U, a diagonal matrix S, and atranspose of an orthogonal matrix V. Hence, the crop yield data set Xmay be decomposed into USV^(t) and expressed as:

$\begin{matrix}{\sum\limits_{k = 1}^{p}{s_{k}u_{k}v_{k}^{t}}} & (4)\end{matrix}$

where s_(k) corresponds to an element in a diagonal matrix S 130; u_(k)corresponds to an element in a matrix U120; and v_(k) ^(t) is an elementin a transposed table V 140.

Using this approach, data values stored in table X110 may beapproximated by computing a product of the corresponding data valuesfrom table U 120, data values of the diagonal matrix S 130, and thetranspose of orthogonal matrix V 140.

In an embodiment, SVD approximation is utilized to produce a set of cropyield values in a time space and a location space. The crop yield valuesare determined from raw crop yield data provided by agriculturalagencies. This may be particularly applicable in situations when the rawcrop yield data values provided by agricultural agencies is incompleteand/or is missing some yield data for some fields and/or for some timepoints. The process may include decomposing table X110 comprising theraw crop yield data values to data values stored in table U 120 and datavalues stored in table V 140, and then using the data derived for tables120, 140 and a diagonal matrix 130 to compute or approximate values thathave not been provided in the raw crop yield data.

For example, assume that table X110 stores raw crop data [x]_(t,l) thatcorresponds to raw crop yield values reported by an agricultural agencyas collected during a year t and a location 1. Based on inspecting thecontent of table X110, it has been determined that table X 110 ismissing crop yield data values for some locations and/or for some timepoints. Determining the values for the missing crop yield readings maybe performed by first decomposing the table X110 into tables U 120 andV140, and then using the values stored in the tables 120 and 140 toapproximate the crop yield values not included in the originallyprovided raw crop yield dataset.

In an embodiment, once raw crop yield data is decomposed into tables U120 and V 140, the information stored in tables U 120 and V 140 may beused to compute, or approximate any missing values in table X110.

Further assume that u_(k) denotes the left singular vector whichcorresponds to the inter-year trend and that v_(k) ^(t) denotes theright singular vector which corresponds to the intra-year spatialcomponent. Additionally, assume that v_(k) ^(t) remains fixed over timefor all k=1 p. Finding particular crop yield values for table X110 mayinclude determining past yield values as well as future yield values.For example, if raw crop yield data has been provided for locationsL_(l) . . . L_(j−1), L_(j+1), . . . L_(k) as the data was collected attime points T₁ . . . T_(n), then the above described approach may allowcomputing any missing crop yield data that has not been provided forlocation L_(j) at any of the times T₁ . . . T_(n), as well as predictingany crop yield data that may be collected in the future (beyond a timepoint T_(n)).

Referring to FIG. 6, if raw crop yield data 150 of crops harvested fromlocation 118 at times 115, 116 and 117 has been provided, then using thedecomposition and approximation approach, a missing crop yield data fortime 119 at location 118 may be reconstructed from the informationstored in the decomposed tables U120 and V140.

Similarly, if raw crop yield data has been provided for any location L₁. . . L_(k) as the data was collected at time points T₁ . . . , T_(j−1),T_(j+1), . . . T_(n), then SVD may be used to predict any crop yielddata harvested at time T_(j), for any of the locations, as well aspredicting any crop yield data that and so forth, as well as to predictany crop yield data may be collected in the future (beyond a time pointT_(n)).

It may be difficult to predict any yield data for a particular locationif less than two crop yield data items has been reported for thatlocation. Indeed, SVD is not usually designed to decompose an entirecolumn of the input matrix if the input matrix has no values, or justone value, for that column. For example, if less than two crop yielddata items is provided, then approximating any crop yield data value fora particular time point may be difficult. Therefore, predicting somefuture yields for time t+1 for a particular location is possible if atleast two or more yield values are available for the particularlocation.

Therefore, an approximation approach is used for predicting a crop yieldvalue for a particular location at a particular time if at least two ormore yield data is available for the particular location for times otherthan the particular time, or if at least two or more yield data isavailable for the particular time point from locations other than theparticular location. Because of these limitations, the approximationapproach allows performing trending of the data representing the cropyield data rather than detrending of the data representing the cropyield data. The approximation allows determining the data valuesaccording to the trends captured at least partially by the volatility ofthe yields. The volatility may depend on a variety of factors, includingweather conditions, soil characteristics, and others.

6. FORECASTING AND RECONSTRUCTING MEASUREMENTS OF YIELDS OF CROPS

In an embodiment, SVD approach is used to model trends in crop yields.The process of modeling the trends may include forecasting orreconstructing measurements of the crop yield data.

FIG. 5 is a flow diagram that depicts an example method for modelingtrends in crop yields. In step 502, electronic digital yield data isreceived. The yield data may represent yields of crops that wereharvested from a plurality of agricultural fields and at a plurality oftime points. The yield data may be received from one or more sources andover one or more computer networks. The yield data may be representedusing data structures that allow storing a yield data value for each ofthe agricultural field from the plurality of agricultural fields and foreach time point from the plurality of time points.

In step 504, the process determines whether a request to generateforecasted yield data or to reconstruct yield data is received. In thisstep, it may be determined, for example, that one or more yield dataitems are to forecasted or reconstructed.

If it is determined in step 504 that a request to generate forecastedyield data or to reconstruct yield data is received, then step 506 isperformed; otherwise, step 514 is performed.

In step 506, one or more factors impacting the yields are determined.

Example factors may include time factors, such as temporal dependenciesbetween agricultural fields from which the crops were harvested. Forinstance, yields collected during a drought may be expected to be lowwhen collected from most of the fields in a given county, while yieldscollected when the weather conditions were significantly favorable areexpected to be high when collected from most of the fields in a givencounty. Furthermore, yields collected at the beginning of the harvestmay have different characteristics than yields collected at the end ofthe harvest. Moreover, yields collected during a rainy weather may havedifferent characteristics than yields collected during a sunny weather,and so forth.

Other factors may represent spatial dependencies between agriculturalfields from which the crops were harvested. For example, yieldscollected from two or more neighboring fields of similar sizes may beexpected to be similar as the soil and other location-based conditionsmay be similar in those fields. In contrast, yields collected from twofields that cannot be considered as neighbors of each other may bedifferent for such fields.

Two or more fields may be determined as neighbors using differenttechniques. For example, neighboring fields may be determined based ontheir respective geographical locations, including determining forexample, whether the fields share common boundaries, or based on certaincharacteristics of the fields and certain agricultural practices,non-limiting examples of which include soil types, hybrid selections,and the like.

There may be also factors that represent both the temporal dependenciesbetween agricultural fields and the spatial dependencies between thefields. Furthermore, there may also be factors that represent othertypes of characteristics that in some way impact the yields of the cropsthat were harvested from the plurality of agricultural fields.

Factor values may be used to decompose a matrix containing the yieldmeasurements into two decomposed matrices as described herein inrelation to FIG. 6. Information included in the decomposed matrices maybe used to generate or reconstruct one or more missing particular yielddata or to modify one or more original yield data items.

In an embodiment, a modified SVD approach that considers both spatialdependencies and temporal dependencies between the harvested crops isused to model crop trends. A particular yield data may be generatedusing an adjacency matrix that is stored in digital computer memory andthat indicates whether a first agricultural field and a secondagricultural field from the plurality of agricultural fields share aborder, or whether the first and the second fields have some similarcharacteristics. Furthermore, a spatial covariance data structure, thatis determined separately for each year represented in the particularyield data, may also be used to generate the particular yield data.

In an embodiment, one of the factors represents temporal dependenciesbetween crop harvesting from one or more agricultural fields from theplurality of agricultural fields. Furthermore, the particular yield datamay be generated using a modified conditional autoregressive approach.

In step 508, decomposed yield data is computed from the measurementdata. A process of decomposing a matrix containing the yield data isdescribed in FIG. 6.

In step 510, based on two or more decomposed yield data items, one ormore particular yield data items are computed. The one or moreparticular yield data items may be used as one or more of: future timeyield data, present time yield data, or past time yield data. Forexample, if a request for determining one or more particular future timeyield data items was received, then the particular future time yielddata item may be generated by incorporating the one or more particularyield data items into the crop yield measurements. According to anotherexample, if a request for determining one or more particular past timeyield data items was received, then the particular past time yield dataitem may be reconstructed by incorporating the one or more particularyield data items into the crop yield measurements.

In step 512, the process reconstructs or generates forecasted yield databy incorporating one or more particular yield data items into the yielddata. For example, if in step 504 it was determined that a request fordetermining one or more particular future time yield data items wasreceived, then in step 512, the process generates forecasted yield databy incorporating the one or more particular yield data items into theyield data. However, if in step 504 it was determined that a request fordetermining one or more particular past time yield data items wasreceived, then in step 512, the process reconstructs the particular pasttime yield data item by incorporating the one or more particular yielddata items into the yield data.

7. MODIFIED CONDITIONAL AUTO REGRESSION

CAR usually applies to observation data that exhibits a first orderdependency or a relatively local spatial autocorrelation. A relativelylocal spatial autocorrelation between the observation data may occur,for example, when the data representing crop yields harvested from aparticular field is influenced in part by crop yield data collected fromimmediately adjacent fields, but not by crop yield data collected fromnon-adjacent fields. The relatively local spatial autocorrelationproperty of such data allows applying CAR to model auto correlatedgeo-referenced crop yield data.

In contrast, a data set representing crop yields may not be locallyspatially correlated if the crop yields for one agricultural fielddepend on characteristics of remote fields. For example, crop yield datacollected from a particular field at a particular time point is notlocally spatially correlated if the crop yield data depends not just onthe conditions influencing the crops harvested from the particularfield, but also on the conditions influencing the crops harvested fromone or more remote fields. Furthermore, crop yield data collected from aparticular field at a particular time point may have no spatialcorrelation at all if the crop yield data is not even dependent on theconditions influencing the crops harvested from the neighboring fieldsthat are local to the particular field.

Under a dynamic system interpretation of CAR model, it is assumed thatthere is an underlying true yield trend and that a reported yieldmeasurement y_(i,t) is drawn from a distribution characterized by thistrend and perturbed by within-year-impulses ε_(i,t). In particular, itmay be assumed that the within-year-impulses ε_(i,t) have a strongspatial auto correlation. The impulses ε_(i,t) can be treated as latentfeatures which mask the underlying yield model.

In an embodiment, CAR model is described using a generative process asfollows:

y _((i,t))+α_(i) t+β _(i)+ε_((i,t))

α_(i)

N(α₀,τ_(α))

β_(i)

N(β₀,τ_(β))

ε_(t)

CAR(θ_(t))

θ_(t)

(0,Σ_(t))  (5)

where y_(i,t) corresponds to crop yield measurements for the yieldharvested from an i-th field and at a t-th time point; “idd” meansindependently and identically distributed random variables; α_(i)corresponds to a linear model variable that depends on the timeparameter, and is computed based on the national trend N observed forall fields or the region, and based on the weather component τ_(α)specific to the all fields or the region; β_(i) corresponds to an offsetvalue for the linear model, and is computed based on the trend Nobserved for a particular region, and based on the weather componentτ_(β) specific to the particular region; ε_(i,t) corresponds to an errorterm that depends on the field-dependent parameter and thetime-dependent parameter, and is computed using the CAR approachdetermined for θ_(t); and θ_(t) is a mean value.

Imposing CAR structure on the covariance structure of the error terms,ε_(i,t) may be determined as:

ε_(t)

N(0,(1−ρ_(c) W)⁻¹τ_(t))  (6)

where W is a binary adjacency matrix indicating whether or not twocounties share a border, and where

τ_(t)=diag(τ_(i,1) ², . . . ,τ_(t,n) ²)  (7)

is a diagonal matrix of squared values of weather component τ_(t,i)specific to the fields, regions, and/or individual counties.

CAR model described in equation (5) may be represented in a platediagram. FIG. 9 is a plate diagram of an example CAR model. Platediagram 900 includes a graphical representation of CAR model 910, andthe process of computing error terms ε_(i,t) and y_(i,t) terms for eachtime point t.

In an embodiment, the model described in equation (5) differs from astandard CAR model. For example, in the model described in equation (5),the spatial covariance structure is conditioned separately for eachyear, which may not be the case for a standard CAR model. Furthermore,the parameters of the model described in equation (5) may be modelleddifferently than it is in a standard CAR model.

The model described in equation (5) may be used to determine trendsrepresented in data capturing yields of crops harvested fromagricultural fields.

8. EXAMPLE OF COMBINED APPROACHES

FIG. 7 is a flow diagram that depicts an example method for modelingtrends in crop yields. In step 702, electronic digital data comprisingyield data is received. The yield data may represent yields of cropsthat were harvested from a plurality of agricultural fields and at aplurality of time points. The yield data may be received from varioussources and over various computer networks. The yield data maycorrespond to measurements or observations described above.

In step 704, the process determines whether a request to generateforecasted yield data or to reconstruct yield data is received. In thisstep, it may be determined that a particular yield data item is missing.It may also be determined, for example, that one or more yield dataitems are to be forecasted or reconstructed.

If it is determined in step 704 that a request to generate forecastedyield data or to reconstruct yield data is received, then step 706 isperformed; otherwise, step 714 is performed.

In step 706, one or more factors impacting the yields harvested from theagricultural fields are determined. The factors may vary and depend onthe implementation of the approach. For example, some factors may betime-dependent, and represent temporal dependencies between agriculturalfields from which the crops were harvested.

Other factors may represent spatial dependencies between agriculturalfields from which the crops were harvested. For example, yieldscollected from two or more neighboring fields of similar sizes may beexpected to be similar as the soil and other location-based conditionsmay be similar in the those fields.

Yet other factors may represent both the temporal dependencies betweenagricultural fields and the spatial dependencies between the fields.Additional examples of the factors are described in FIG. 5.

In step 750, it is determined whether the factors impacting the yieldsharvested from the agricultural fields are time-independent. If all thefactors are time-independent, then step 760 is performed. Otherwise,step 770 is performed.

In step 760, a value decomposition approach or algorithm is selectedfrom a group of available value decomposition algorithms. Examples ofthe value decomposition approaches or algorithms are described for FIG.5. For example, SVD approach or modified SVD approach may be selected instep 760.

In step 765, the selected decomposition approach is used to decompose amatrix containing the received raw yield data. For example, decomposedyield data may be computed from the measurement data. A process ofdecomposing a matrix containing the yield data is described in FIG. 6.

Based on one or more decomposed yield data, one or more particular yielddata items are computed. The one or more particular yield data items maybe used as one or more of: future time yield data, present time yielddata, or past time yield data. For example, if a request for determiningone or more particular future time yield data items was received, thenthe particular future time yield data items may be forecasted byincorporating the one or more particular yield data items into the yielddata. According to another example, if a request for determining one ormore particular past time yield data items was received, then theparticular past time yield data items may be reconstructed byincorporating the one or more particular yield data items into the yielddata.

In step 712, the process generates forecasted yield data or reconstructsyield data by incorporating one or more particular yield data items intothe yield data. For example, if in step 704 it was determined that arequest for determining one or more particular future time yield dataitems was received, then in step 712, the process generates forecastedyield data by incorporating the one or more particular yield data itemsinto the yield data. The one or more particular yield data items may beused to predict the yield of crops harvested in the future. However, ifin step 704 it was determined that a request for determining one or moreparticular past time yield data items was received, then in step 712,the process reconstructs the particular past time yield data items byincorporating the one or more particular yield data items into the yielddata.

However, if one or more factors impacting the yields harvested from theagricultural fields are time-dependent, then step 770 is performed.

In step 770, a conditional auto-regressive approach or algorithm isselected from a group of available auto-regressive algorithms. Examplesof the auto-regressive algorithms are described in FIG. 9. For example,a CAR approach or a modified CAR approach may be selected in step 770.

In step 780, the selected auto-regression approach is used to determineone or more particular yield data items. The one or more particularyield data may be used as one or more of: future time yield data,present time yield data, or past time yield data. Once the one or moreparticular yield data items are determined, step 712 is performed. Step712 is described above.

The approach described in FIG. 7 allows generating forecasted yield dataand/or reconstructing a set of raw yield data by taking intoconsideration one or more factors impacting the yields harvested fromthe agricultural fields are determined. The approach allows taking intoaccount some factors that are time-dependent, and represent temporaldependencies between agricultural fields from which the crops wereharvested, as well as some factors that are time-independent.

9. EXAMPLE RESULT ANALYSIS

FIG. 8 is a data plot that depicts an example modeling function of cropyields. Plot 800 is a two-dimensional graph depicting an example ofyields of crops harvested from a plurality of fields (locations) and ata plurality of time points. The plurality of time points is representedalong a time horizontal axis 810, while the plurality of locations isrepresented along a location vertical axis 820.

As depicted in FIG. 8, approximating a trend 830 based on theobservation may be difficult as it may be unknown whether thefluctuation in data values was a function of weather, and if so, to whatdegree. Furthermore, some of the data points may be incorrect as thecorresponding measurements may be influenced by specific time-dependentand time-independent factors.

As shown in FIG. 8, data points 822, 824, 826 and 828 were used todetermine a trend characteristic 830. Trend characteristic 830approximates the data points 822, 824, 826 and 828. However, if forexample, a data point 822 is incorrect because it was disproportionatelyinfluenced by the unexpected weather conditions, then the data point 822may incorrectly influence the overall trend characteristic 830.Correcting the value of the data point 822 could be used to de-trend thetrend characteristic 830.

Applications of models such as SVD model, modified SVD model, CAR modeland/or modified SVD models may allow improving the quality of the trendapproximation. By modeling the time-dependent factors and thetime-independent factors using at least some of the above approaches,the trend approximation may be more accurate and reliable.

In an embodiment, the decomposition and then the approximation of dataitems representing yields of corps allow removing or modifying the yielddata items that appear to be incorrect. The incorrect data are oftenreferred to as noise data, or just noise. The noise data may be removedusing the above described approaches, and thus the impact of variousfactors, such as the weather conditions, may be eliminated to somedegree. Using this approach, the trend characteristics may be correctedand the resulting trends may more adequately represent the yield trends.

10. ADDITIONAL APPROACHES

In addition to the approaches and algorithms described above, othermethods may also be utilized to determine trends for the yields of cropsharvested from agricultural fields. Some of the methods are brieflydescribed below.

10.1. Mean Model Approach

In an embodiment, a process for determining trends for the yields ofcrops harvested from agricultural fields is determined using a meanmodel algorithm. A mean model approach assumes that the crop yields fromeach county may be modeled separately using the following equation:

y _(i,t)=β_(i)+∈_(i,t)

∈_(i,t)

N(0,σ_(i) ²)  (8)

where y_(i,t) corresponds to crop yield measurements for the yieldharvested from an i-th field and at a t-th time point; “idd” meansindependently and identically distributed random variables; β_(i)corresponds to an offset value for the mean model for an i-th field;where ε_(i,t) corresponds to an error term that depends on afield-dependent parameter and a time-dependent parameter.

A mean model may be used to reconstruct or predict y_(i,t) values bysimply determining an average value of the yields harvested from thefields within a county. This may be represented as:

ŷ _(i) ,T−1= y _(i).  (9)

10.2. LINEAR MODEL APPROACH

In an embodiment, a process for determining trends for the yields ofcrops harvested from agricultural fields is determined using a linearmodel algorithm. A linear model approach assumes that the crop yieldsfrom each county exhibit a linear trend and may be modeled separatelyusing the following equation:

y _(i,t)=α_(i) t+β _(i)+∈_(i,t)

∈_(i,t)

N(0,σ_(i) ²  (10)

where y_(i,t) corresponds to crop yield measurements for the yieldharvested from an i-th field and at a t-th time point; where “idd” meansindependently and identically distributed random variables; where α_(i)corresponds to a linear model variable whose value depends on an i-thfield; where β_(i) corresponds to an offset value for the linear modelfor an i-th field; where ε_(i,t) corresponds to an error term thatdepends on a field-dependent parameter and a time-dependent parameter.

10.3. Robust Linear Model Approach

In an embodiment, a process for determining trends for the yields ofcrops harvested from agricultural fields is determined using a robustlinear model algorithm. This model uses the equation for the linearmodel presented above, except that the values for the slope andintercept are computed using M-estimators. M-estimators are theestimators that are obtained as the minima of sums of functions of thedata. Examples of M-estimators include least-squares estimators,weighted least-squares estimators, and the like.

10.4. Smoothing Splines Approach

In an embodiment, a process for determining trends for the yields ofcrops harvested from agricultural fields is determined using a smoothingsplines algorithm. A smoothing spline model assumes:

y _(i,t) +f(x _(i,t))+∈_(i,t)

∈_(i,t)

N(0,σ²)  (11)

where y_(i,t) corresponds to crop yield measurements for the yieldharvested from an i-th field and at a t-th time point; where f(⋅) is asmoothing function. A penalize regression approach may be used toestimate f(⋅); where “idd” means independently and identicallydistributed random variables; where ε_(i,t) corresponds to an error termthat depends on a field-dependent parameter and a time-dependentparameter.

A smoothing splines approach allows a user to determine the smoothnessof the estimated function. However, usually a more automatic selectionmethod is used to establish the smoothness parameter. For example, ageneralized cross validation (GSV) estimate may be used instead.

A smoothing splines approach is usually well suited for interpolatingmissing data. However, is some situations, the method may exhibit ahigher bias at the boundaries. This is because the method assumes alinear behavior when extrapolating the data; this assumption is oftenincorrect.

10.5. Quadratic Model Approach

In an embodiment, a process for determining trends for the yields ofcrops harvested from agricultural fields is determined using a quadraticmodel algorithm. A quadratic model assumes that yields are quadratic intime, and may be represented using the following equation:

y _(i,t)=α_(i) t ²−γ_(i) t=β _(i)=∈_(i,t)  (12)

where y_(i,t) corresponds to crop yield measurements for the yieldharvested from an i-th field and at a t-th time point; α_(i) correspondsto a quadratic model variable whose value depends on an i-th field andis multiplied by a square of the time value; γ corresponds to aquadratic model variable whose value depends on an i-th field and ismultiplied by the time value; β_(i) corresponds to an offset value foran i-th field; ε_(i,t) corresponds to an error term that depends on afield-dependent parameter and a time-dependent parameter.

10.6. Locally Weighted Regression Approach

In an embodiment, a process for determining trends for the yields ofcrops harvested from agricultural fields is determined using a locallyweighted regression model algorithm. A locally weighted regression modela regression model to a single county using data points from the currentcounty and its surrounding neighbors. The neighborhood for a givencounty is defined using a distance measure, such as the distance betweencounties centers. The county that is further away from a center of thecurrent modeled county may have less influence on the output of themodel.

10.7. Integrated Moving Average Approach

In an embodiment, a process for determining trends for the yields ofcrops harvested from agricultural fields is determined using anintegrated moving average (IMA) model algorithm. This model assumes thatthe yield series y_(i,t) is non-stationary. The approach takes intoconsideration the lagged series that can be corrected. Using thisapproach, the difference series is modelled as a moving-average.

IMA approach is often a useful model for economic time series. The IMAapproach may be used to forecast the current local mean value. Forexample, the equation x_(t+1)=x mean_(t)+ε_(t+1) may be used to performa one-step forecast is x mean at t, which is the current local mean.

10.8. Random Walk

In an embodiment, a process for determining trends for the yields ofcrops harvested from agricultural fields is determined using a randomwalk model algorithm. This model assumes that yields follow a randomwalk model:

y _(i,t) =y _(i,t−1)+∈_(i,t)  (13)

where y_(i,t) corresponds to crop yield measurements for the yieldharvested from an i-th field and at a t-th time point; y_(i,t+1)corresponds to crop yield measurements for the yield harvested from ani-th field and a t+1 th time point; ε_(i,t) corresponds to an error termthat depends on a field-dependent parameter and a time-dependentparameter.

The predicted value may be determined as:

ŷ _(i,T+1) +y _(i,T)  (14)

10.9. Multivariate Adaptive Regression Splines Model

In an embodiment, a process for determining trends for the yields ofcrops harvested from agricultural fields is determined using amultivariate adaptive regression splines (MARS) model algorithm. MARSalgorithm is a non-parametric regression technique that can be seen asan extension of linear models that automatically model non-linearitiesand interactions between variables. MARS is a piecewise linear modelthat can be represented as:

y _(i,t)=α_(i)[τ_(i) −t]₊+γ_(i)[t−τ _(i)]₊+β_(i)+∈_(i,t)  (15)

where y_(i,t) corresponds to crop yield measurements for the yieldharvested from an i-th field and a t-th time point; [t−τ_(i)]₊ is alinear spline and τ_(i) is the knot.

MARS model is used to detect and fit change points in linear trends. Themodel can be greatly extended to include several change points. However,the MARS suffers from drawbacks similar to those in any smoother method.For extrapolation purposes it assumes a linear trend which may not bevalid.

11. BENEFITS OF CERTAIN EMBODIMENTS

The techniques described herein offer a coherent and robust approach formodeling trends in yields of crops harvested from agricultural fields.For example, using a presented decomposition approach and then anapproximation approach, future yields can be predicted.

Presented decomposition approach improves the efficiency of computerresources used to model trends in crop yields. By decomposing large setsof collected measurements data into relatively small subsets and thenprocessing the data from the small subsets, not the large sets, smalleramounts of computer resources such as CPU, computer memory and networkbandwidth are used. The efficient way of processing the collectedmeasurements and using the resulting models allow an efficient use ofcomputer equipment installed in harvester combines. For example, datafrom the resulting models may be used to correct information collectedin the field, and the resulting data may be used to control and enhancethe way agricultural fields are cultivated. The corrected informationmay be used to drive or control agricultural equipment or computersystems controlling the equipment. Data included in the resulting modelsmay be used to develop recommendations for the agricultural systems andplanting and fertilizing equipment. Furthermore, the data included inthe resulting models may be displayed on display devices of computersystems used to control the crop planting, fertilizing and harvesting.

Using the presented approaches, yield data that have not been providedfor the harvested yields may be approximated or otherwise determined.The presented approach also allows correcting errors in the yields dataprovided by various agencies and from various sources.

In addition, the data decomposition and approximation techniquesdescribed herein allow interpreting various dynamics and dependencies ofthe yields harvested from collocated agricultural fields.

Furthermore, applying the presented approach to raw crop yieldmeasurements, to approximate spatial and temporal dependencies of cropyields harvested from agricultural fields, allows analyzing the cropyield data in the context of independencies between variouscharacteristics specific to not just individual fields or locations, butalso groups of fields and regions. For example, it allows determiningspatial relationships between the measurements data collected fromneighboring fields. The spatial relationships may include the impact theweather conditions may have on the neighboring fields, the irrigationconditions impacting the crop yields collected from the irrigatedfields, and the like.

What is claimed is:
 1. A method comprising: using data receivinginstructions programmed in a computer system comprising one or moreprocessors and computer memory, sending one or more requests to one ormore remote sensors installed on agricultural equipment to provide, overa computer network, electronic digital data comprising yield datarepresenting yields of crops that were harvested from a plurality ofagricultural fields and at a plurality of time points; using the datareceiving instructions in the computer system, receiving over a computernetwork, in response to sending one or more requests to one or moreremote sensors installed on an agricultural equipment, yield data fromone or more remote sensors and representing yields of crops that wereharvested from a plurality of agricultural fields and at a plurality oftime points; using yield dependency instructions in the computer system,determining one or more factors that impact yields of crops that wereharvested from the plurality of agricultural fields; using datadecomposition instructions in the computer system, decomposing the yielddata into decomposed yield data that identifies one or more datadependencies according to the one or more factors; using dataapproximation instructions in the computer system, generating, based onthe decomposed yield data, one or more particular yield data using amodified singular value decomposition that uses at least one factor thatwas determined for one or more items in the yield data; wherein theparticular yield data includes fewer values than the yield data; usingdata reconstruction instructions in the computer system, generating anddisplaying forecasted yield data by processing and incorporating theparticular yield data into the yield data.
 2. The method of claim 1,wherein at least one factor of the one or more factors that impactsyields of crops that were harvested from the plurality of agriculturalfields is time independent and represents spatial dependencies betweentwo or more agricultural fields from the plurality of agriculturalfields; wherein the generating forecasted yield data by processing andincorporating the particular yield data, which includes fewer valuesthan the yield data, uses less computational and storage resources thandetermining forecasted yield using the yield data; wherein the methodfurther comprises displaying the forecasted yield data on a display ofthe computer system to control crop planting, fertilizing andharvesting.
 3. The method of claim 1, wherein at least one factor of theone or more factors that impacts yields of crops that were harvestedfrom the plurality of agricultural fields is time dependent andrepresents temporal dependencies between crop harvesting from one ormore agricultural fields from the plurality of agricultural fields;wherein the method further comprises generating the particular yielddata using a modified conditional autoregression using the at least onefactor determined for one or more items in the yield data.
 4. The methodof claim 3, further comprising generating the particular yield datausing an adjacency matrix that is stored in digital computer memory andthat indicates whether a first agricultural field and a secondagricultural field from the plurality of agricultural fields share aborder and using a spatial covariance data structure that is determinedseparately for each year represented in the particular yield data. 5.The method of claim 3, further comprising generating the particularyield data using regional yield trend data that is specific to one ormore fields of the plurality of agricultural fields and national yieldtrend data.
 6. The method of claim 3, further comprising generating theparticular yield data using one or more weather variability componentsand one or more weather patterns that are specific to the plurality ofagricultural fields.
 7. The method of claim 1, further comprising usingthe decomposed yield data to interpret spatial and temporal dependenciesbetween yields of crops that were harvested from the plurality ofagricultural fields and to determine how weather conditions and regionadjacencies influence yields.
 8. The method of claim 1, wherein theparticular yield data is generated to obtain one or more of: future timeyield data, present time yield data, or past time yield data.
 9. Themethod of claim 1, wherein the particular yield data for a particulartime point and a particular agricultural field is used to verify anaccuracy of the yield data received for the particular time point andthe particular agricultural field.
 10. The method of claim 1, furthercomprising generating a yield data model by implementing one or more ofmean models, a linear model, a robust linear model, a smooth spline, aquadratic model, a locally weighted regression model, an integratedmoving average model, a random walk model, a multivariate adaptiveregression splines model, an SVD standard model, or a CAR standardmodel.
 11. A data processing system comprising: a memory; one or moreprocessors coupled to the memory and programmed to: send one or morerequests to one or more remote sensors installed on agriculturalequipment to provide, over a computer network, electronic digital datacomprising yield data representing crop yields harvested from aplurality of agricultural fields and at a plurality of time points; inresponse to sending the one or more requests to the one or more remotesensors installed on the agricultural equipment, receive over thecomputer network the electronic digital data comprising yield data; inresponse to receiving input specifying a request to generate particularyield data: determine one or more factors that impact yields of cropsthat were harvested from the plurality of agricultural fields; decomposethe yield data into decomposed yield data that identifies one or moredata dependencies according to the one or more factors; wherein theparticular yield data includes fewer values than the yield data;generate, based on the decomposed yield data, the particular yield datausing a modified singular value decomposition that uses at least onefactor that was determined for one or more items in the yield data;generate forecasted yield data by processing and incorporating theparticular yield data into the yield data.
 12. The data processingsystem of claim 11, wherein at least one factor of the one or morefactors that impacts yields of crops that were harvested from theplurality of agricultural fields is time independent and representsspatial dependencies between two or more agricultural fields from theplurality of agricultural fields; wherein the generating forecastedyield data by processing and incorporating the particular yield data,which includes fewer values than the yield data, uses less computationaland storage resources than determining forecasted yield using the yielddata; wherein the one or more processors coupled to the memory arefurther programmed to display the forecasted yield data on a display ofthe computer system to control crop planting, fertilizing andharvesting.
 13. The data processing system of claim 11, wherein at leastone factor of the one or more factors that impacts yields of crops thatwere harvested from the plurality of agricultural fields is timedependent and represents temporal dependencies between crop harvestingfrom one or more agricultural fields from the plurality of agriculturalfields; wherein the one or more processors coupled to the memory arefurther programmed to generate the particular yield data using amodified conditional autoregression using the at least one factordetermined for one or more items in the yield data.
 14. The dataprocessing system of claim 13, wherein the one or more processorscoupled to the memory are further programmed to generate the particularyield data using an adjacency matrix that is stored in digital computermemory and that indicates whether a first agricultural field and asecond agricultural field from the plurality of agricultural fieldsshare a border, and using a spatial covariance data structure that isdetermined separately for each year represented in the particular yielddata.
 15. The data processing system of claim 13, wherein the one ormore processors coupled to the memory are further programmed to generatethe particular yield data using regional yield trend data that isspecific to one or more fields of the plurality of agricultural fieldsand national yield trend data.
 16. The data processing system of claim13, wherein the one or more processors coupled to the memory are furtherprogrammed to generate the particular yield data using one or moreweather variability components and one or more weather patterns that arespecific to the plurality of agricultural fields.
 17. The dataprocessing system of claim 11, wherein the one or more processorscoupled to the memory are further programmed to use the decomposed yielddata to interpret spatial and temporal dependencies between yields ofcrops that were harvested from the plurality of agricultural fields andto determine how weather conditions and region adjacencies influenceyields.
 18. The data processing system of claim 11, wherein theparticular yield data is generated to obtain one or more of: future timeyield data, present time yield data, or past time yield data.
 19. Thedata processing system of claim 11, wherein the particular yield datafor a particular time point and a particular agricultural field is usedto verify an accuracy of the yield data received for the particular timepoint and the particular agricultural field.
 20. The data processingsystem of claim 11, wherein the one or more processors coupled to thememory are further programmed to generate a yield data model byimplementing one or more of mean models, a linear model, a robust linearmodel, a smooth spline, a quadratic model, a locally weighted regressionmodel, an integrated moving average model, a random walk model, amultivariate adaptive regression splines model, an SVD standard model,or a CAR standard model.